写这个是因为有童鞋在跑VGG的时候遇到各种问题，供参考一下。
网络结构
以VGG16为例，自己跑的细胞数据
solver.prototxt：
net: "/media/dl/source/Experiment/cell/test/vgg/vgg16.prototxt" test_iter: 42 test_interval: 1000 base_lr: 0.0001 lr_policy: "step" gamma: 0.1 stepsize: 100000 display: 200 max_iter: 200000 momentum: 0.9 weight_decay: 0.0005 snapshot: 100000 snapshot_prefix: "/media/dl/source/Experiment/cell/test/vgg/vgg" solver_mode: GPU
vgg16.prototxt：
注意，这里的数据层我是用的“ImageData”格式，也就是没有转为LMDB，直接导入图片进去的，因为我用的服务器，为了方便。如果为了更高效，还是使用LMDB数据库的形式。使用LMDB数据库形式的数据层我也写了下，放在这个prototxt后面作为补充。
另外，注意修改最后一个全连接层的num_output为自己的类别数。并修改该层的名字，如我改为了“cellfc8”，是为了finetune vgg时重新训练该层，不使用该层的预训练参数。
1 name: "VGG16"
2 layer {
3 name: "data"
4 type: "ImageData"
5 top: "data"
6 top: "label"
7 include {
8 phase: TRAIN
9 }
10 # transform_param {
11 # mirror: true
12 # crop_size: 224
13 # mean_file: "data/ilsvrc12_shrt_256/imagenet_mean.binaryproto"
14 # }
15
16 image_data_param {
17 source: "/media/dl/source/Experiment/cell/data/trainnew2_resize/trainnew.txt"
18 batch_size: 20
19 shuffle:true
20 #is_color: false
21 new_height: 224
22 new_width: 224
23 }
24 }
25 layer {
26 name: "data"
27 type: "ImageData"
28 top: "data"
29 top: "label"
30 include {
31 phase: TEST
32 }
33 # transform_param {
34 # mirror: false
35 # crop_size: 224
36 # mean_file: "data/ilsvrc12_shrt_256/imagenet_mean.binaryproto"
37 # }
38
39 image_data_param {
40 source: "/media/dl/source/Experiment/cell/data/val2_resize/valnew.txt"
41 batch_size: 50
42 #is_color: false
43 new_height: 224
44 new_width: 224
45 }
46 }
47 layer {
48 bottom: "data"
49 top: "conv1_1"
50 name: "conv1_1"
51 type: "Convolution"
52 param {
53 lr_mult: 1
54 decay_mult: 1
55 }
56 param {
57 lr_mult: 2
58 decay_mult: 0
59 }
60 convolution_param {
61 num_output: 64
62 pad: 1
63 kernel_size: 3
64 weight_filler {
65 type: "gaussian"
66 std: 0.01
67 }
68 bias_filler {
69 type: "constant"
70 value: 0
71 }
72 }
73 }
74 layer {
75 bottom: "conv1_1"
76 top: "conv1_1"
77 name: "relu1_1"
78 type: "ReLU"
79 }
80 layer {
81 bottom: "conv1_1"
82 top: "conv1_2"
83 name: "conv1_2"
84 type: "Convolution"
85 param {
86 lr_mult: 1
87 decay_mult: 1
88 }
89 param {
90 lr_mult: 2
91 decay_mult: 0
92 }
93 convolution_param {
94 num_output: 64
95 pad: 1
96 kernel_size: 3
97 weight_filler {
98 type: "gaussian"
99 std: 0.01
100 }
101 bias_filler {
102 type: "constant"
103 value: 0
104 }
105 }
106 }
107 layer {
108 bottom: "conv1_2"
109 top: "conv1_2"
110 name: "relu1_2"
111 type: "ReLU"
112 }
113 layer {
114 bottom: "conv1_2"
115 top: "pool1"
116 name: "pool1"
117 type: "Pooling"
118 pooling_param {
119 pool: MAX
120 kernel_size: 2
121 stride: 2
122 }
123 }
124 layer {
125 bottom: "pool1"
126 top: "conv2_1"
127 name: "conv2_1"
128 type: "Convolution"
129 param {
130 lr_mult: 1
131 decay_mult: 1
132 }
133 param {
134 lr_mult: 2
135 decay_mult: 0
136 }
137 convolution_param {
138 num_output: 128
139 pad: 1
140 kernel_size: 3
141 weight_filler {
142 type: "gaussian"
143 std: 0.01
144 }
145 bias_filler {
146 type: "constant"
147 value: 0
148 }
149 }
150 }
151 layer {
152 bottom: "conv2_1"
153 top: "conv2_1"
154 name: "relu2_1"
155 type: "ReLU"
156 }
157 layer {
158 bottom: "conv2_1"
159 top: "conv2_2"
160 name: "conv2_2"
161 type: "Convolution"
162 param {
163 lr_mult: 1
164 decay_mult: 1
165 }
166 param {
167 lr_mult: 2
168 decay_mult: 0
169 }
170 convolution_param {
171 num_output: 128
172 pad: 1
173 kernel_size: 3
174 weight_filler {
175 type: "gaussian"
176 std: 0.01
177 }
178 bias_filler {
179 type: "constant"
180 value: 0
181 }
182 }
183 }
184 layer {
185 bottom: "conv2_2"
186 top: "conv2_2"
187 name: "relu2_2"
188 type: "ReLU"
189 }
190 layer {
191 bottom: "conv2_2"
192 top: "pool2"
193 name: "pool2"
194 type: "Pooling"
195 pooling_param {
196 pool: MAX
197 kernel_size: 2
198 stride: 2
199 }
200 }
201 layer {
202 bottom: "pool2"
203 top: "conv3_1"
204 name: "conv3_1"
205 type: "Convolution"
206 param {
207 lr_mult: 1
208 decay_mult: 1
209 }
210 param {
211 lr_mult: 2
212 decay_mult: 0
213 }
214 convolution_param {
215 num_output: 256
216 pad: 1
217 kernel_size: 3
218 weight_filler {
219 type: "gaussian"
220 std: 0.01
221 }
222 bias_filler {
223 type: "constant"
224 value: 0
225 }
226 }
227 }
228 layer {
229 bottom: "conv3_1"
230 top: "conv3_1"
231 name: "relu3_1"
232 type: "ReLU"
233 }
234 layer {
235 bottom: "conv3_1"
236 top: "conv3_2"
237 name: "conv3_2"
238 type: "Convolution"
239 param {
240 lr_mult: 1
241 decay_mult: 1
242 }
243 param {
244 lr_mult: 2
245 decay_mult: 0
246 }
247 convolution_param {
248 num_output: 256
249 pad: 1
250 kernel_size: 3
251 weight_filler {
252 type: "gaussian"
253 std: 0.01
254 }
255 bias_filler {
256 type: "constant"
257 value: 0
258 }
259 }
260 }
261 layer {
262 bottom: "conv3_2"
263 top: "conv3_2"
264 name: "relu3_2"
265 type: "ReLU"
266 }
267 layer {
268 bottom: "conv3_2"
269 top: "conv3_3"
270 name: "conv3_3"
271 type: "Convolution"
272 param {
273 lr_mult: 1
274 decay_mult: 1
275 }
276 param {
277 lr_mult: 2
278 decay_mult: 0
279 }
280 convolution_param {
281 num_output: 256
282 pad: 1
283 kernel_size: 3
284 weight_filler {
285 type: "gaussian"
286 std: 0.01
287 }
288 bias_filler {
289 type: "constant"
290 value: 0
291 }
292 }
293 }
294 layer {
295 bottom: "conv3_3"
296 top: "conv3_3"
297 name: "relu3_3"
298 type: "ReLU"
299 }
300 layer {
301 bottom: "conv3_3"
302 top: "pool3"
303 name: "pool3"
304 type: "Pooling"
305 pooling_param {
306 pool: MAX
307 kernel_size: 2
308 stride: 2
309 }
310 }
311 layer {
312 bottom: "pool3"
313 top: "conv4_1"
314 name: "conv4_1"
315 type: "Convolution"
316 param {
317 lr_mult: 1
318 decay_mult: 1
319 }
320 param {
321 lr_mult: 2
322 decay_mult: 0
323 }
324 convolution_param {
325 num_output: 512
326 pad: 1
327 kernel_size: 3
328 weight_filler {
329 type: "gaussian"
330 std: 0.01
331 }
332 bias_filler {
333 type: "constant"
334 value: 0
335 }
336 }
337 }
338 layer {
339 bottom: "conv4_1"
340 top: "conv4_1"
341 name: "relu4_1"
342 type: "ReLU"
343 }
344 layer {
345 bottom: "conv4_1"
346 top: "conv4_2"
347 name: "conv4_2"
348 type: "Convolution"
349 param {
350 lr_mult: 1
351 decay_mult: 1
352 }
353 param {
354 lr_mult: 2
355 decay_mult: 0
356 }
357 convolution_param {
358 num_output: 512
359 pad: 1
360 kernel_size: 3
361 weight_filler {
362 type: "gaussian"
363 std: 0.01
364 }
365 bias_filler {
366 type: "constant"
367 value: 0
368 }
369 }
370 }
371 layer {
372 bottom: "conv4_2"
373 top: "conv4_2"
374 name: "relu4_2"
375 type: "ReLU"
376 }
377 layer {
378 bottom: "conv4_2"
379 top: "conv4_3"
380 name: "conv4_3"
381 type: "Convolution"
382 param {
383 lr_mult: 1
384 decay_mult: 1
385 }
386 param {
387 lr_mult: 2
388 decay_mult: 0
389 }
390 convolution_param {
391 num_output: 512
392 pad: 1
393 kernel_size: 3
394 weight_filler {
395 type: "gaussian"
396 std: 0.01
397 }
398 bias_filler {
399 type: "constant"
400 value: 0
401 }
402 }
403 }
404 layer {
405 bottom: "conv4_3"
406 top: "conv4_3"
407 name: "relu4_3"
408 type: "ReLU"
409 }
410 layer {
411 bottom: "conv4_3"
412 top: "pool4"
413 name: "pool4"
414 type: "Pooling"
415 pooling_param {
416 pool: MAX
417 kernel_size: 2
418 stride: 2
419 }
420 }
421 layer {
422 bottom: "pool4"
423 top: "conv5_1"
424 name: "conv5_1"
425 type: "Convolution"
426 param {
427 lr_mult: 1
428 decay_mult: 1
429 }
430 param {
431 lr_mult: 2
432 decay_mult: 0
433 }
434 convolution_param {
435 num_output: 512
436 pad: 1
437 kernel_size: 3
438 weight_filler {
439 type: "gaussian"
440 std: 0.01
441 }
442 bias_filler {
443 type: "constant"
444 value: 0
445 }
446 }
447 }
448 layer {
449 bottom: "conv5_1"
450 top: "conv5_1"
451 name: "relu5_1"
452 type: "ReLU"
453 }
454 layer {
455 bottom: "conv5_1"
456 top: "conv5_2"
457 name: "conv5_2"
458 type: "Convolution"
459 param {
460 lr_mult: 1
461 decay_mult: 1
462 }
463 param {
464 lr_mult: 2
465 decay_mult: 0
466 }
467 convolution_param {
468 num_output: 512
469 pad: 1
470 kernel_size: 3
471 weight_filler {
472 type: "gaussian"
473 std: 0.01
474 }
475 bias_filler {
476 type: "constant"
477 value: 0
478 }
479 }
480 }
481 layer {
482 bottom: "conv5_2"
483 top: "conv5_2"
484 name: "relu5_2"
485 type: "ReLU"
486 }
487 layer {
488 bottom: "conv5_2"
489 top: "conv5_3"
490 name: "conv5_3"
491 type: "Convolution"
492 param {
493 lr_mult: 1
494 decay_mult: 1
495 }
496 param {
497 lr_mult: 2
498 decay_mult: 0
499 }
500 convolution_param {
501 num_output: 512
502 pad: 1
503 kernel_size: 3
504 weight_filler {
505 type: "gaussian"
506 std: 0.01
507 }
508 bias_filler {
509 type: "constant"
510 value: 0
511 }
512 }
513 }
514 layer {
515 bottom: "conv5_3"
516 top: "conv5_3"
517 name: "relu5_3"
518 type: "ReLU"
519 }
520 layer {
521 bottom: "conv5_3"
522 top: "pool5"
523 name: "pool5"
524 type: "Pooling"
525 pooling_param {
526 pool: MAX
527 kernel_size: 2
528 stride: 2
529 }
530 }
531 layer {
532 bottom: "pool5"
533 top: "fc6"
534 name: "fc6"
535 type: "InnerProduct"
536 param {
537 lr_mult: 1
538 decay_mult: 1
539 }
540 param {
541 lr_mult: 2
542 decay_mult: 0
543 }
544 inner_product_param {
545 num_output: 4096
546 weight_filler {
547 type: "gaussian"
548 std: 0.005
549 }
550 bias_filler {
551 type: "constant"
552 value: 0.1
553 }
554 }
555 }
556 layer {
557 bottom: "fc6"
558 top: "fc6"
559 name: "relu6"
560 type: "ReLU"
561 }
562 layer {
563 bottom: "fc6"
564 top: "fc6"
565 name: "drop6"
566 type: "Dropout"
567 dropout_param {
568 dropout_ratio: 0.5
569 }
570 }
571 layer {
572 bottom: "fc6"
573 top: "fc7"
574 name: "fc7"
575 type: "InnerProduct"
576 param {
577 lr_mult: 1
578 decay_mult: 1
579 }
580 param {
581 lr_mult: 2
582 decay_mult: 0
583 }
584 inner_product_param {
585 num_output: 4096
586 weight_filler {
587 type: "gaussian"
588 std: 0.005
589 }
590 bias_filler {
591 type: "constant"
592 value: 0.1
593 }
594 }
595 }
596 layer {
597 bottom: "fc7"
598 top: "fc7"
599 name: "relu7"
600 type: "ReLU"
601 }
602 layer {
603 bottom: "fc7"
604 top: "fc7"
605 name: "drop7"
606 type: "Dropout"
607 dropout_param {
608 dropout_ratio: 0.5
609 }
610 }
611 layer {
612 bottom: "fc7"
613 top: "fc8"
614 name: "cellfc8"
615 type: "InnerProduct"
616 param {
617 lr_mult: 1
618 decay_mult: 1
619 }
620 param {
621 lr_mult: 2
622 decay_mult: 0
623 }
624 inner_product_param {
625 num_output: 7 #改为自己的类别数
626 weight_filler {
627 type: "gaussian"
628 std: 0.005
629 }
630 bias_filler {
631 type: "constant"
632 value: 0.1
633 }
634 }
635 }
636 layer {
637 name: "accuracy_at_1"
638 type: "Accuracy"
639 bottom: "fc8"
640 bottom: "label"
641 top: "accuracy_at_1"
642 accuracy_param {
643 top_k: 1
644 }
645 include {
646 phase: TEST
647 }
648 }
649 layer {
650 name: "accuracy_at_5"
651 type: "Accuracy"
652 bottom: "fc8"
653 bottom: "label"
654 top: "accuracy_at_5"
655 accuracy_param {
656 top_k: 5
657 }
658 include {
659 phase: TEST
660 }
661 }
662 layer {
663 bottom: "fc8"
664 bottom: "label"
665 top: "loss"
666 name: "loss"
667 type: "SoftmaxWithLoss"
668 }
如果使用LMDB数据库形式，将前面的数据层改为：
1 name: "vgg"
2 layer {
3 name: "data"
4 type: "Data"
5 top: "data"
6 top: "label"
7 include {
8 phase: TRAIN
9 }
10 transform_param {
11 mirror: true
12 crop_size: 224
13 #如果图片大于224，则使用crop的方式，小于则使用下面的new_height和new_width
14 # new_height: 224
15 #new_width: 224
16 mean_file: "vggface/face_mean.binaryproto"
17 }
18 data_param {
19 source: "vggface/face_train_lmdb"
20 batch_size: 20
21 backend: LMDB
22 }
23 }
24 layer {
25 name: "data"
26 type: "Data"
27 top: "data"
28 top: "label"
29 include {
30 phase: TEST
31 }
32 transform_param {
33 mirror: false
34 crop_size: 224
35 #如果图片大于224，则使用crop的方式，小于则使用下面的new_height和new_width
36 # new_height: 224
37 #new_width: 224
38 mean_file: "vggface/face_mean.binaryproto"
39 }
40 data_param {
41 source: "vggface/face_val_lmdb"
42 batch_size: 20
43 backend: LMDB
44 }
45 }
训练
放一个shell命令：
#!/usr/bin/env sh TOOLS=/home/dl/caffe-jonlong/build/tools $TOOLS/caffe train -solver=/media/dl/source/Experiment/cell/test/vgg/solver.prototxt -weights=/media/dl/source/Experiment/cell/test/vgg/VGG_ILSVRC_16_layers.caffemodel -gpu=all \
预训练模型VGG_ILSVRC_16_layers.caffemodel的下载地址为
http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel
原文地址：https://www.cnblogs.com/go-better/p/9053973.html
时间： 2024-10-05 20:17:49